Types Of Distortions Research Articles

Synthesis, Structure, Spectral, Redox, and Theoretical Studies of Sterically Crowded Decaaryl Triphyrin(2.1.1)s.

First examples of sterically crowded β- and meso-arylated decaaryl triphyrin(2.1.1)s were synthesized by reacting β-hexabromo meso-tetraaryl triphyrin(2.1.1) with five different aryl boronic acids in THF/toluene/H2O (1 : 1 : 1) in the presence of catalytic amount of Pd(PPh3)4/Na2CO3 at 80 °C for 12-24 h. The X-ray structure obtained for one of the decaaryl triphyrin(2.1.1)s revealed that the macrocycle exhibited saddle type of distortion due to steric crowding by bulky aryl substituents introduced at the six β-carbons of meso-tetraaryl triphyrin(2.1.1) unlike almost planar structure observed for β-unsubstituted meso-tetraaryl triphyrin(2.1.1). The absorption bands of decaaryl triphyrin(2.1.1)s were broadened and bathochromically shifted compared to meso-tetraaryl triphyrin(2.1.1) and the redox potentials were dependent on the kind of aryl substituents present at the β-pyrrole carbons in decaaryl triphyrin(2.1.1)s. DFT and TD-DFT studies helped in understanding the alteration in the structure, spectral, and redox properties in decaaryl triphyrin(2.1.1)s compared to meso-tetraaryl triphyrin(2.1.1).

Understanding secondary order parameters in perovskites with tilted octahedra

In the family of perovskite materials, the tilts of BX 6 octahedra are the most common type of structural distortion. Conventionally, the formation of low-symmetry perovskite phases with tilted octahedra is analyzed by considering only primary order parameters. However, octahedral tilting also gives rise to secondary order parameters which contribute to additional atomic displacements, ordering and lattice distortions. Our study highlights the significant impact of secondary order parameters on the structural formation and emergent physical properties of perovskites. Through group-theoretical and crystallographic analyses, we have identified all secondary order parameters within Glazer-type tilt systems and clarified their physical manifestations. We explore the fundamental symmetry relationships among various structural degrees of freedom in perovskites, including tilt-induced ferroelasticity, correlations between displacements and ordering of atoms occupying different positions, and the potential for rigid unit rotations and unconventional octahedral tilts. Particular emphasis is placed on the emergence of secondary order parameters and their coupling with primary order parameters, as well as their symmetry-based hierarchy, illustrated through a modified Bärnighausen tree. We applied our theoretical insights to elucidate phase transitions in well known perovskites such as CaTiO3 and RMnO3 (where R = La and lanthanide ions), thereby demonstrating the significant influence of secondary order parameters on crystal structure formation. Our results serve as a symmetry-based guide for the design, identification and structural characterization of perovskites with tilted octahedra, and for understanding tilt-induced physical properties.

Comparison of the Impacts of Background Distortion with Even and Odd Orders in the Supraharmonic Range on Grid-Connected Converters

In this paper, a comparison of the emissions of grid-connected converters under grid distortion in the supraharmonic range is performed. The investigated supraharmonic grid distortion is divided into two types, namely odd-integer and even-integer multiples of the grid frequency. It is found that the first distortion type does not affect the emissions of the grid-connected converters, while the second distortion is responsible for the 2N-harmonic emissions that appear in the frequency spectra of the grid-connected converters, which, in turn, interact with the supraharmonic primary emissions of these converters, resulting in additional supraharmonic emissions. To explain this behavior, a mathematical analysis is provided for two types of grid-connected converters with an inverter interface and with a diode bridge rectifier interface. Simulations and experimental studies are performed to verify the main findings of the mathematical analysis.

ADAPTIVE METHOD OF FORMING COMPLEX SIGNALS ENSEMBLES BASED ON MULTI-LEVEL RECURRENT TIME-FREQUENCY SEGMENT MODELING

The article investigates the implementation of an adaptive method for forming ensembles of complex signals based on multilevel recurrent time-frequency segmentation. It addresses the key challenges faced by cognitive wireless networks operating in dynamic radio frequency environments with high levels of interference, necessitating rapid adaptation to changes in the spectral characteristics of signals. The study substantiates the need for adaptive filters and specific transformations to enhance signal processing quality, particularly in environments with high variability in frequency characteristics and significant noise interference. The proposed method of multilevel recurrent time-frequency segmentation allows for the modification of time segment durations and the use of segments of varying lengths, providing flexibility in signal processing and adaptation to current conditions. This adaptability enables optimal signal processing for each individual case, taking into account short-term impulses, long-term fluctuations, and various types of noise and distortions. This approach effectively separates frequency components and reduces interference between them, which is crucial for maintaining high signal quality and communication stability in cognitive networks. It has been proven that the use of adaptive filters such as LMS (Least Mean Squares) and RLS (Recursive Least Squares), as well as fast Fourier Transform (STFT), wavelet, and Hilbert transforms at different stages of multilevel time-frequency segmentation, significantly enhances signal interference resistance and energy efficiency. Comparative analysis of signal metrics before and after filtering and transformation shows an increase in signal quality by 14,3–24,5% and a reduction in noise levels by 21,7–29,6%. The wavelet transform, in particular, proved to be highly effective, allowing for precise extraction of useful frequency components from the noise background and improving signal parameters through dynamic adjustment to specific radio environment conditions. Experimental results confirm the effectiveness of the proposed method, demonstrating its ability to ensure consistently high-quality processing of complex signal ensembles even in dynamic cognitive radio environments.

Mediating Effect of Perceived Personal Luck in the Relationship Between Narcissism and Problem Gambling in Strategic Gamblers: An Exploratory Study.

Gambling is becoming increasingly frequent among the French population, which has led to an increased number of individuals exhibiting problematic gambling patterns. Studies have found links between narcissism and cognitive distortions in relation to problem gambling, specifically among strategic gamblers, but none has tested perceived personal luck as one type of cognitive distortion in this regard. This study's purpose was to explore the relationship between narcissism, perceived personal luck and problem gambling among strategic gamblers, and to test for the mediating role of perceived personal luck in the relationship between narcissism and problem gambling. A total of 88 regular strategic gamblers were included (mean age = 37.8), with 49 non-problematic gamblers and 39 problematic gamblers. Participants completed the Problematic Gambling Severity Index, the Narcissistic Personality Inventory, and the Personal Luck Usage Scale. Results showed a significant relationship between narcissism and problem gambling, but no significant link between perceived personal luck and problem gambling and no significant mediation effect of perceived personal luck. These results may indicate a strong perception of personal skills compared to a weak perception of personal luck among problem gamblers of strategic games. This study emphasizes the importance of addressing types of gambling and types of cognitive distortions when investigating problem gambling.

Overview of High-Dynamic-Range Image Quality Assessment.

In recent years, the High-Dynamic-Range (HDR) image has gained widespread popularity across various domains, such as the security, multimedia, and biomedical fields, owing to its ability to deliver an authentic visual experience. However, the extensive dynamic range and rich detail in HDR images present challenges in assessing their quality. Therefore, current efforts involve constructing subjective databases and proposing objective quality assessment metrics to achieve an efficient HDR Image Quality Assessment (IQA). Recognizing the absence of a systematic overview of these approaches, this paper provides a comprehensive survey of both subjective and objective HDR IQA methods. Specifically, we review 7 subjective HDR IQA databases and 12 objective HDR IQA metrics. In addition, we conduct a statistical analysis of 9 IQA algorithms, incorporating 3 perceptual mapping functions. Our findings highlight two main areas for improvement. Firstly, the size and diversity of HDR IQA subjective databases should be significantly increased, encompassing a broader range of distortion types. Secondly, objective quality assessment algorithms need to identify more generalizable perceptual mapping approaches and feature extraction methods to enhance their robustness and applicability. Furthermore, this paper aims to serve as a valuable resource for researchers by discussing the limitations of current methodologies and potential research directions in the future.

An Exploratory Study of Cognitive Distortions and Psychosis among Meth-Amphetamine Users

This research aims at studying the cognitive distortions and psychosis among the meth-amphetamine users. This study was exploratory in nature and qualitative research design was used. The sample size of this study was N = 19 meth-amphetamine users who were residing in different rehabilitation centers of Islamabad/Rawalpindi and convenient sampling technique was used. Semi-structured interviews were conducted and then the thematic analysis was used to analyze the data. The results of the analysis indicates that there is a strong association between cognitive distortions and psychotic symptoms. The results also highlighted that the most common type of cognitive distortions includes all or nothing thinking, mental filtering, and catastrophizing. This study has shown that hallucinations and paranoid delusions can cause significant harm to their day-to-day functioning as well as to their quality of life. The research highlight the critical role that social and emotional support plays in mitigating the impact of cognitive distortions on mental illness. The results of this study highlights the need of comprehensive intervention techniques which will target both the cognitive and psychosocial levels in the treatment of addiction to prevent the relapse rate.

TDMD: A Database for Dynamic Color Mesh Quality Assessment Study.

Dynamic colored meshes (DCM) are widely used in various applications. However, this kind of meshes may undergo different processes, such as compression or transmission, which can distort them and degrade their quality. To facilitate the development of objective metrics for DCMs and study the influence of typical distortions on their perception, we create the Tencent - Dynamic colored Mesh Database (TDMD) containing eight reference DCM objects with six typical distortions. Using processed video sequences (PVS) derived from the DCM, we conduct a large-scale subjective experiment that resulted in 303 distorted DCM samples with mean opinion scores, making the TDMD the largest available DCM database to our knowledge. This database enables us to study the impact of different types of distortion on human perception and offers recommendations for DCM compression and related tasks. Additionally, we have evaluated three types of state-of-the-art objective metrics on the TDMD, including image-based, point-based, and video-based metrics, on the TDMD. Our experimental results highlight the strengths and weaknesses of each metric, and we provide suggestions about the selection of metrics in practical DCM applications. The TDMD is publicly available at the following location: https://multimedia.tencent.com/resources/tdmd.

Empowering quadrature mirror filter bank architectures: dynamic-grey wolf optimization approach for elevated filter orders

The present study introduces a revised version of the Grey Wolf Optimization (GWO) algorithm called Dynamic-GWO, which integrates a dynamic parameter adaptation mechanism for population diversity enhancement. This mechanism implements the real-time adjustment of control parameters throughout the optimization procedure. The algorithm's effectiveness is demonstrated by its consistent performance across a variety of intricate benchmark functions. Further, the suggested approach is employed to determine the eminent filter tap coefficients of the Quadrature-Mirror-Filter (QMF) bank that enables the configuration with filter orders of 120 and 150, which have yet to be achieved using existing methods. The uniqueness of the proposed approach lies in its capability to overcome the inherent difficulties associated with higher-order filter design, including increased filter length and computational complexity. The primary objective function is to minimize the algebraic sum of three types of distortion: pass-band error, excess power of stop-band, and squared error of the overall transfer function at the quadrature frequency ω q = π / 2 , which generally occur in the procedure of Near-Perfect-Reconstruction (NPR) QMF bank design. In addition, the measured maximum reconstruction errors are 1.303 × 10 − 15 , 2.93 × 10 − 16 and 2.18 × 10 − 06 dB, and aliasing distortions are A L = 85.80 , 128.49 and 153.52 dB for filter orders N = 100, 120 and 150, correspondingly. The proposed algorithm yields significant improvements with an 86.68% reduction in the ripple factor and a 10.45% increase in attenuation compared to the baseline GWO for filter order 120, underscoring its effectiveness in enhancing filter performance. The observed outcomes indicate that the Dynamic-GWO algorithm consistently surpasses the baseline GWO and other Nature-Inspired Algorithms (NIAs) regarding computational ease and convergence speed.

GPA-Net:No-Reference Point Cloud Quality Assessment With Multi-Task Graph Convolutional Network.

With the rapid development of 3D vision, point cloud has become an increasingly popular 3D visual media content. Due to the irregular structure, point cloud has posed novel challenges to the related research, such as compression, transmission, rendering and quality assessment. In these latest researches, point cloud quality assessment (PCQA) has attracted wide attention due to its significant role in guiding practical applications, especially in many cases where the reference point cloud is unavailable. However, current no-reference metrics which based on prevalent deep neural network have apparent disadvantages. For example, to adapt to the irregular structure of point cloud, they require preprocessing such as voxelization and projection that introduce extra distortions, and the applied grid-kernel networks, such as Convolutional Neural Networks, fail to extract effective distortion-related features. Besides, they rarely consider the various distortion patterns and the philosophy that PCQA should exhibit shift, scaling, and rotation invariance. In this paper, we propose a novel no-reference PCQA metric named the Graph convolutional PCQA network (GPA-Net). To extract effective features for PCQA, we propose a new graph convolution kernel, i.e., GPAConv, which attentively captures the perturbation of structure and texture. Then, we propose the multi-task framework consisting of one main task (quality regression) and two auxiliary tasks (distortion type and degree predictions). Finally, we propose a coordinate normalization module to stabilize the results of GPAConv under shift, scale and rotation transformations. Experimental results on two independent databases show that GPA-Net achieves the best performance compared to the state-of-the-art no-reference PCQA metrics, even better than some full-reference metrics in some cases.

ColorVideoVDP: A visual difference predictor for image, video and display distortions

ColorVideoVDP is a video and image quality metric that models spatial and temporal aspects of vision for both luminance and color. The metric is built on novel psychophysical models of chromatic spatiotemporal contrast sensitivity and cross-channel contrast masking. It accounts for the viewing conditions, geometric, and photometric characteristics of the display. It was trained to predict common video-streaming distortions (e.g., video compression, rescaling, and transmission errors) and also 8 new distortion types related to AR/VR displays (e.g., light source and waveguide non-uniformities). To address the latter application, we collected our novel XR-Display-Artifact-Video quality dataset (XR-DAVID), comprised of 336 distorted videos. Extensive testing on XR-DAVID, as well as several datasets from the literature, indicate a significant gain in prediction performance compared to existing metrics. ColorVideoVDP opens the doors to many novel applications that require the joint automated spatiotemporal assessment of luminance and color distortions, including video streaming, display specification, and design, visual comparison of results, and perceptually-guided quality optimization. The code for the metric can be found at https://github.com/gfxdisp/ColorVideoVDP.

Performance Investigation of 17 Level Reduced Switch Count Multilevel Inverter

The primary objective of this paper is to introduce a 17-level multilevel inverter with only eight power switches and three diodes that can be suitable for electric vehicle applications. This setup utilizes three distinct unequal DC sources to create the 17 levels of output voltage waveforms. The modes of operations of the proposed topology have been discussed in detail. The calculation of conduction losses, switching losses, efficiency, total standing voltage, and cost function per level for the suggested inverter has been elaborated. Due to more semiconductor power switches, diodes, capacitors, driver circuits, and DC sources in typical inverters, switching losses, costs, and harmonic distortion are increased. The nearest-level control technique has been utilized to control the switching elements of the recommended configuration. The performance comparison of various multilevel inverter topologies has also been discussed. The suggested multilevel inverter provides a higher efficiency of 98.60%, cost function of 3.6 for a weight coefficient of 1, improved power quality, and higher reliability. A reduction in the power switches significantly reduces the convolution of switching circuitry. The total harmonic distortion produced by this inverter is 3.49%, which comes under the IEEE standard of 5%.

Perceptual point cloud qality assessment for immersive metaverse experience

Perceptual quality assessment for point cloud is critical for immersive metaverse experience and is a challenging task. Firstly, because point cloud is formed by unstructured 3D points that makes the topology more complex. Second, the quality impairment generally involves both geometric attributes and color properties, where the measurement of the geometric distortion becomes more complex. We proposed a perceptual point cloud quality assessment model that follows the perceptual features of Human Visual System (HVS) and the intrinsic characteristics of the point cloud. The point cloud is first pre-processed to extract the geometric skeleton keypoints with graph filtering-based re-sampling, and local neighboring regions around the geometric skeleton keypoints are constructed by K-Nearest Neighbors (KNN) clustering. For geometric distortion, the Point Feature Histogram (PFH) is extracted as the feature descriptor, and the Earth Mover's Distance (EMD) between the PFHs of the corresponding local neighboring regions in the reference and the distorted point clouds is calculated as the geometric quality measurement. For color distortion, the statistical moments between the corresponding local neighboring regions are computed as the color quality measurement. Finally, the global perceptual quality assessment model is obtained as the linear weighting aggregation of the geometric and color quality measurement. The experimental results on extensive datasets show that the proposed method achieves the leading performance as compared to the state-of-the-art methods with less computing time. Meanwhile, the experimental results also demonstrate the robustness of the proposed method across various distortion types. The source codes are available at https://github.com/llsurreal919/PointCloudQualityAssessment.

Investigating the angular distortion impact on vehicular optical camera communication (OCC) systems

Optical camera communication (OCC) shows promise for optical wireless communication (OWC) in vehicular networks. However, vehicle mobility-induced angular distortions hinder system throughput by degrading non-isotropic vehicular OCC channel gain. Few of the prior works have ever made a comprehensive analysis of their impact, especially based on the pixel value which reflects the camera imaging features. To address this knowledge gap, a pixel value-described vehicular OCC system model accounting for transmitter imaging location and intensity from the geometry and radiometry aspects is presented in this paper with common types of the offset and rotation angles included. We integrate a MATLAB-based simulated vehicular OCC system with an experimentally designed testbed for validation and performance analysis. For a single-time snapshot, we investigate the impacts of common angular distortion types in vehicular OCC systems on maximum pixel value, imaging location, and communication-related metrics. Furthermore, we statistically analyze their influences by considering two driving scenarios with respective angular distributions. The angular distortion characterization from this work is expected to lay a stepping stone to addressing mobility in vehicular OCC systems.

Effects of a Serious Game for Adolescent Mental Health on Cognitive Vulnerability: Pilot Usability Study.

Adolescent mental health is of utmost importance. E-mental health interventions, and serious games in particular, are appealing to adolescents and can have beneficial effects on their mental health. A serious game aimed at improving cognitive vulnerability (ie, beliefs or attitudes), which can predispose an individual to mental health problems, can contribute to the prevention of these problems in adolescents. This study aimed to assess the feasibility of the prototype of a serious game called "Silver." The prototype of the serious game was developed using a user-centered participatory design. The prototype of Silver focused on 1 aspect of a serious game for improving cognitive vulnerability in adolescents, that is, the recognition and identification of cognitive distortions. Through the game, players were required to identify and classify the character's thoughts as helpful or unhelpful. Upon successful advancement to the next level, the task becomes more challenging, as players must also identify specific types of cognitive distortions. A pre- and posttest uncontrolled design was used to evaluate the game, with a 1-week intervention phase in which participants were asked to play the game. Participants aged 12-16 years were recruited in schools. The outcomes of interest were the recognition of cognitive distortions and presence of participants' cognitive distortions. The game was also evaluated on its effects, content, and usefulness. A total of 630 adolescents played Silver and completed the assessments. Adolescents were significantly better at recognizing cognitive distortions at the pretest (mean 13.09, SD 4.08) compared to the posttest (mean 13.82, SD 5.09; t629=-4.00, P<.001). Furthermore, their cognitive distortions decreased significantly at the posttest (mean 38.73, SD 12.79) compared to the pretest (mean 41.43, SD 10.90; t629=7.98, P<.001). Participants also indicated that the game helped them recognize cognitive distortions. Many participants considered the game appealing (294/610, 48.2%) but boring (317/610, 52%) and preferred a more comprehensive game (299/610, 49%). Findings from this study suggest that a serious game may be an effective tool for improving cognitive vulnerability in adolescents. The development of such a serious game, based on the prototype, is recommended. It may be an important and innovative tool for the universal prevention of mental health problems in adolescents. Future research on the effects of the game is warranted.

Do cognitive abilities reduce eyewitness susceptibility to the misinformation effect? A systematic review.

The fact that memories can be distorted by post-event misinformation has cast considerable doubt over the dependability of eyewitness evidence in legal contexts. However, despite its adverse practical implications, the misinformation effect is likely an unavoidable distortion stemming from the reconstructive nature of episodic memory. Certain cognitive abilities have been reported to offer protection against misinformation, suggesting that mechanisms aside from episodic memory may also be underpinning this type of memory distortion. The purpose of this review was to collate findings of associations between eyewitness misinformation susceptibility and individual differences in cognitive ability in adults aged 18 and over. Nine studies met the eligibility criteria for this review, including 23 distinct associations. Using a narrative synthesis, three categories of cognitive ability were identified as influencing susceptibility to misinformation: general intelligence and reasoning, perceptual abilities, and memory abilities. Across almost all categories, higher levels of ability were associated with reduced susceptibility to misinformation. While there is no indication that any one trait provides total immunity to the misinformation effect, there is a reasonable amount of evidence to suggest that several cognitive abilities create variance in individual levels of susceptibility. Future research should investigate not only if but how these cognitive abilities protect against misinformation distortions, for example, by contributing to more detailed encoding of the memory, enhancing discrepancy detection in the face of post-event misinformation, or improving source-monitoring during an eyewitness memory test.

Assessing Face Image Quality: A Large-Scale Database and a Transformer Method.

The amount of face images has been witnessing an explosive increase in the last decade, where various distortions inevitably exist on transmitted or stored face images. The distortions lead to visible and undesirable degradation on face images, affecting their quality of experience (QoE). To address this issue, this paper proposes a novel Transformer-based method for quality assessment on face images (named as TransFQA). Specifically, we first establish a large-scale face image quality assessment (FIQA) database, which includes 42,125 face images with diversifying content at different distortion types. Through an extensive crowdsource study, we obtain 712,808 subjective scores, which to the best of our knowledge contribute to the largest database for assessing the quality of face images. Furthermore, by investigating the established database, we comprehensively analyze the impacts of distortion types and facial components (FCs) on the overall image quality. Accordingly, we propose the TransFQA method, in which the FC-guided Transformer network (FT-Net) is developed to integrate the global context, face region and FC detailed features via a new progressive attention mechanism. Then, a distortion-specific prediction network (DP-Net) is designed to weight different distortions and accurately predict final quality scores. Finally, the experiments comprehensively verify that our TransFQA method significantly outperforms other state-of-the-art methods for quality assessment on face images.

PDenoiser: A Personalized Speech Enhancement Neural Network for Pre-hospital Emergency Medical Services.

Pre-hospital emergency medical service (EMS) tasks often come with complex and diverse noise interferences, posing challenges in implementing ASR-based medical technologies and hindering efficient and accurate telephonic communication. Among the different types of noise distortion, interfering speech is especially annoying. To address these issues, our aim is to develop a technology capable of extracting the intended speech content of the target physician from noisy and mixed audio during EMS tasks. In this work, we propose a monoaural personalized speech enhancement (PSE) method called pDenoiser, which is a real-time neural network that operates in the time domain. By leveraging the prior vocalization cues of emergency physicians, pDenoiser selectively enhances target speech components while suppressing noise and nontarget speech components, thereby improving speech quality and speech recognition accuracy under noisy conditions. We demonstrate the potential value of our approach through evaluations on both public general-domain test sets and our self-collected real-world EMS test sets. The experimental results are promising, as our model effectively promotes both speech quality and ASR performance under various conditions and outperforms related methods across multiple evaluation metrics. Our methodology will hopefully elevate EMS efficiency and fortify security against nontarget speech during EMS tasks.

Reward-Based Crowdfunding Versus Initial Coin Offerings

Inspired by emerging financing techniques that offer additional financing options for entrepreneurs, we establish a stylized model to compare two prevalent crowdfunding strategies: Reward-based crowdfunding and Initial Coin Offerings (ICOs). First, we highlight that the fundamental difference between reward-based crowdfunding and ICO lies in the distinct types of price distortion, resulting in different agency costs. Specifically, compared to the first-best benchmark, reward-based crowdfunding sets a lower funding price to attract more consumers to pre-order, while ICO sets a higher market price due to speculators’ revenue-sharing in the market stage. Second, we show that the financing limit (i.e., the maximum funds raised) of reward-based crowdfunding is driven by the platform traffic, whereas that of ICO is driven by the marginal production cost. Therefore, even with numerous speculators, ICO may result in a lower financing limit than reward-based crowdfunding when the platform traffic or the marginal production cost is high. Third, we explore how products’ cost structures shape entrepreneurs’ financing preferences. ICO suits intangible products with high fixed cost and low marginal production cost, while reward-based crowdfunding aligns better with products exhibiting the opposite cost structure. Finally, we extend our model to show the robustness of our findings and offer some additional insights. For example, ICO may amplify the risk of market uncertainty compared to reward-based crowdfunding; Security Token Offerings (STO) benefits from the absence of agency costs between the entrepreneur and speculators compared to ICO while it may suffer from equity loss, particularly in risky and regulated markets, etc.

Scene recovery: Combining visual enhancement and resolution improvement

Visibility enhancement of outdoor images under complex imaging conditions has been a crucial task for computer vision and received growing attention. However, existing image enhancement methods could result in typical block-like artifacts or color distortion. The undesirable impurities might also be significantly magnified after the enhancement task, further reducing the image quality. For enhancing and super-resolving complex real-world degradation, we propose a simultaneous visual enhancement and resolution improvement (VERI) variational scene recovery model for jointly enhancing image visibility and improving the resolution of the degraded image. Particularly, we estimate the scattering light map for degradation images to achieve clean scene radiance and simultaneously seek a high-quality image through a deep super-resolution network. The semi-proximal alternating direction method of multipliers (sPADMM) algorithm is employed for efficiently solving the minimization problems in the proposed model. Extensive experiments illustrate the effectiveness and robustness of the proposed method in dealing with various scenes, such as haze, sandstorm, underwater or low illumination.